A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment

Ji, Daan; Wang, Chuang; Li, Jiahui; Dong, Hongli

doi:10.1080/21642583.2021.1992684

Cited by 47 publications

(15 citation statements)

References 214 publications

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“…On top of sensor problems, intermittent connectivity and the lack of forward sensing capabilities can result in missing data. Several studies have listed the development of specialized feature selection/extraction as a challenging task in PHM [3,8,250]. A wide range of the present articles identify and later eliminate redundant input signals from different sensors.…”

Section: Challenges and Outlookmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

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<ul> <li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br> (RUL)<br> </li> <li>It discusses challenges and open problems faced in PdM<br> </li> <li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br> </li> <li>We propose the future direction for each challenge discussed in this article</li> </ul>

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Section: Challenges and Outlookmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

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“…In an imbalanced dataset, numerous data points are available that resemble healthy conditions. However, only a handful of data points denote faulty states [8,13,14,250]. As a result, adequate infrastructure [249], and highly optimized algorithms that can handle such large volumes of (potentially imbalanced) data [17] are required to address this challenge.…”

Section: Challenges and Outlookmentioning

confidence: 99%

“…Not all data are good data. Sensor anomalies and low-quality data [12,13,15,250], including misreadings [12], signal interference, disconnections, and isolated issues, account for the majority of problems faced in PdM. The instances mentioned in the NFF or IFs can be partially attributed to sensor anomalies.…”

Section: Challenges and Outlookmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

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show abstract

“…In certain circumstances, the variance differences are not indeed maximized, and the computed variances fail to represent the most discriminative features of a class, leading to some limitations in feature extraction. However, to the best of author's knowledge, no research has investigated this defect of basic CSP in its principles [17]. Therefore, the objective of this paper is to cope with the problem of inaccurate objective functions of the CSP algorithm.…”

Section: Channel Selectionmentioning

confidence: 99%

“…Due to the characteristics of EEG signals mentioned above, the conventional time-domain, frequency-domain, and time-frequency-domain analysis to extract EEG features could have some limitations [17]. These methods are generally unsupervised, while there is no definite rule or paradigm to decide the parameters used in these methods [18].…”

Section: Introductionmentioning

confidence: 99%

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Yang

Huang

2022

IJNDI

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Survey/review study Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,* 1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom 2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States * Correspondence: Mengjie.Huang@liverpool.ac.uk Received: 12 October 2022 Accepted: 28 November 2022 Published: 22 December 2022 Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.

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A review: data driven-based fault diagnosis and RUL prediction of petroleum machinery and equipment

Cited by 47 publications

References 214 publications

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

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